基于多策略多目标差分进化算法的风光储系统协调优化调度

doi:10.16182/j.issn1004731x.joss.23-1216

摘要/Abstract

摘要：

新能源发电单元的引入，使得电力系统组成结构愈发复杂，现有经济调度求解方法遇到诸多挑战。构建了一个风光储系统协调动态经济调度模型，给出了一种利用修补策略的约束条件处理 方法，提出了一种基于竞争机制的多策略多目标差分进化算法（competitive partitioning-based multi-strategy multi-objective differential evolutionary algorithm，CMMODE）。利用竞争机制将种群分区，并根据分区结果构建多个差分变异算子，以此生成多策略方案；采用精英自探索机制令种群具有跳出局部最优能力与局部开发能力。仿真实验验证了CMMODE算法在解决动态经济调度问题上的有效性，约束处理方法具有良好的可行性。

关键词: 差分进化算法, 竞争机制, 多策略方案, 约束处理, 动态经济调度

Abstract:

The introduction of new energy generation units makes the power system structure more and more complex, and the existing economic dispatching methods face many challenges. A coordinated and optimal dispatching for wind-photovoltaic-storage systems is constructed and a constraint handling method is given, a competitive mechanism-based multi-strategy multi-objective differential evolutionary(CMMODE) algorithm is proposed. The CMMODE algorithm utilizes a competitive mechanism to partition the population and constructs multiple differential variance operators based on the partitioning results, thus generating a multi-strategy scheme, employs an elite self-exploration mechanism to make the population have the ability to jump out of the local optimum and the ability of exploitation. The results show the effectiveness of the CMMODE algorithm in solving the dynamic economic dispatch problem, and the constraint processing method has good feasibility.

Key words: differential evolution algorithms, competitive mechanisms, multi-strategy schemes, constraint processing, dynamic economic dispatch

中图分类号:

TP391.9

任旭阳,卜旭辉,尹艳玲等 . 基于多策略多目标差分进化算法的风光储系统协调优化调度[J]. 系统仿真学报, 2025, 37(2): 450-461.

Ren Xuyang,Bu Xuhui,Yin Yanling,et al . Coordinated and Optimal Dispatching for Wind-photovoltaic-storage Systems Based on Multi-strategy Multi-objective Differential Evolution Algorithm[J]. Journal of System Simulation, 2025, 37(2): 450-461.

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参考文献 26

1	刘兴华, 耿晨, 谢胜寒, 等. 考虑光伏发电不确定性的日前火电-光伏经济调度[J]. 系统仿真学报, 2022, 34(8): 1874-1884.
	Liu Xinghua, Geng Chen, Xie Shenghan, et al. Day Ahead Thermal-photovoltaic Economic Dispatch Considering Uncertainty of Photovoltaic Power Generation[J]. Journal of System Simulation, 2022, 34(8): 1874-1884.
2	Yan Ning, Xing Zuoxia, Li Wei, et al. Economic Dispatch Application of Power System with Energy Storage Systems[J]. IEEE Transactions on Applied Superconductivity, 2016, 26(7): 1-5.
3	刘芸, 韩松, 黄秋立. 基于实时电价响应行为的源荷协调日前调度模型[J]. 系统仿真学报, 2021, 33(5): 1196-1204.
	Liu Yun, Han Song, Huang Qiuli. Day-ahead Dispatching Model of Source-load Coordination Based on Response Behavior to Real-time Pricing[J]. Journal of System Simulation, 2021, 33(5): 1196-1204.
4	Dong Yingchao, Zhang Hongli, Wang Cong, et al. An Adaptive State Transition Algorithm with Local Enhancement for Global Optimization[J]. Applied Soft Computing, 2022, 121: 108733.
5	魏乐, 李希金. 基于多目标的含风电场动态环境经济调度[J]. 系统仿真学报, 2020, 32(9): 1825-1830.
	Wei Le, Li Xijin. Dynamic Environmental and Economic Dispatching of Wind Farm Based on Multi-objective[J]. Journal of System Simulation, 2020, 32(9): 1825-1830.
6	Nourianfar Hossein, Abdi Hamdi. Economic Emission Dispatch Considering Electric Vehicles and Wind Power Using Enhanced Multi-objective Exchange Market Algorithm[J]. Journal of Cleaner Production, 2023, 415: 137805.
7	谭碧飞, 陈皓勇, 梁子鹏, 等. 基于协同NSGA-Ⅱ的微电网随机多目标经济调度[J]. 高电压技术, 2019, 45(10): 3130-3139.
	Tan Bifei, Chen Haoyong, Liang Zipeng, et al. Stochastic Multi-objective Economic Dispatch of Micro-grid Based on CoNSGA-Ⅱ[J]. High Voltage Engineering, 2019, 45(10): 3130-3139.
8	Amorim Elizete A, Rocha Carlos. Optimization of Wind-thermal Economic-emission Dispatch Problem Using NSGA-III[J]. IEEE Latin America Transactions, 2020, 18(9): 1555-1562.
9	王庆, 李玉琛, 蒙飞, 等. 混沌正余弦算法在含风能电力系统经济排放调度中的应用[J]. 电力系统保护与控制, 2022, 50(24): 172-177.
	Wang Qing, Li Yuchen, Meng Fei, et al. Application of an Improved Chaotic Sine Cosine Algorithm in the Economic Emission Scheduling of a Power System with Wind Energy[J]. Power System Protection and Control, 2022, 50(24): 172-177.
10	Kennedy J, Eberhart R. Particle Swarm Optimization[C]//Proceedings of ICNN'95-International Conference on Neural Networks. Piscataway: IEEE, 1995: 1942-1948.
11	Liang J J, Qin Ak, Suganthan P N, et al. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 281-295.
12	帅茂杭, 熊国江, 胡晓, 等. 基于改进多目标骨干粒子群算法的电力系统环境经济调度[J]. 控制与决策, 2022, 37(4): 997-1004.
	Maohang Shuai, Xiong Guojiang, Hu Xiao, et al. Economic Emission Dispatch of Power System Based on Improved Bare-bone Multi-objective Particle Swarm Optimization Algorithm[J]. Control and Decision, 2022, 37(4): 997-1004.
13	Cheng Ran, Jin Yaochu. A Competitive Swarm Optimizer for Large Scale Optimization[J]. IEEE Transactions on Cybernetics, 2015, 45(2): 191-204.
14	Storn R, Price K. Differential Evolution——A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces[J]. Journal of Global Optimization, 1997, 23(1): 341-359.
15	Liang Jing, Xu Weiwei, Yue Caitong, et al. Multimodal Multiobjective Optimization with Differential Evolution[J]. Swarm and Evolutionary Computation, 2019, 44: 1028-1059.
16	邓武, 蔡幸, 周永权, 等. 一种基于多策略差分进化的分解多目标进化算法[J]. 控制与决策, 2022, 37(2): 387-392.
	Deng Wu, Cai Xing, Zhou Yongquan, et al. A Novel Decomposition Multi-objective Evolutionary Algorithm Based on Differential Evolution Model with Multi-strategy[J]. Control and Decision, 2022, 37(2): 387-392.
17	Derong Lü, Xiong Guojiang, Fu Xiaofan. Economic Emission Dispatch of Power Systems Considering Solar Uncertainty with Extended Multi-objective Differential Evolution[J]. Expert Systems with Applications, 2023, 227: 120298.
18	梅盼盼, 吴亮红, 张红强, 等. 含风电电力系统动态经济调度的差分纵横交叉优化[J]. 系统仿真学报, 2020, 32(6): 1179-1187.
	Mei Panpan, Wu Lianghong, Zhang Hongqiang, et al. Adaptive Differential Crisscross Optimization Algorithm for Dynamic Economic Emission Dispatch Considering Wind Power[J]. Journal of System Simulation, 2020, 32(6): 1179-1187.
19	Das Swagatam, Sankha Subhra Mullick, Suganthan P N. Recent Advances in Differential Evolution-an Updated Survey[J]. Swarm and Evolutionary Computation, 2016, 27: 1-30.
20	张伟, 黄卫民. 基于种群分区的多策略自适应多目标粒子群算法[J]. 自动化学报, 2022, 48(10): 2585-2599.
	Zhang Wei, Huang Weimin. Multi-strategy Adaptive Multi-objective Particle Swarm Optimization Algorithm Based on Swarm Partition[J]. Acta Automatica Sinica, 2022, 48(10): 2585-2599.
21	梁静, 葛士磊, 瞿博阳, 等. 求解电力系统经济调度问题的改进粒子群优化算法[J]. 控制与决策, 2020, 35(8): 1813-1822.
	Liang Jing, Ge Shilei, Qu Boyang, et al. Improved Particle Swarm Optimization Algorithm for Solving Power System Economic Dispatch Problem[J]. Control and Decision, 2020, 35(8): 1813-1822.
22	Mason Karl, Duggan Jim, Howley Enda. A Multi-objective Neural Network Trained with Differential Evolution for Dynamic Economic Emission Dispatch[J]. International Journal of Electrical Power & Energy Systems, 2018, 100: 201-221.
23	Coello Coello C A, Lechuga M S. MOPSO: A Proposal for Multiple Objective Particle Swarm Optimization[C]//Proceedings of the 2002 Congress on Evolutionary Computation. Piscataway: IEEE, 2002: 1051-1056.
24	Deb K, Pratap A, Agarwal S, et al. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
25	Ali Musrrat, Siarry Patrick, Pant Millie. An Efficient Differential Evolution Based Algorithm for Solving Multi-objective Optimization Problems[J]. European Journal of Operational Research, 2012, 217(2): 404-416.
26	Pan J S, Liu Nengxian, Chu Shuchuan. A Competitive Mechanism Based Multi-objective Differential Evolution Algorithm and Its Application in Feature Selection[J]. Knowledge-Based Systems, 2022, 245: 108582.

参数	值	参数	值
额定容量/MW	400	充电效率	0.95
额定功率/MW	200	荷电状态	[0, 1]
放电效率	0.95	调度始末的SOC	10

系统	算法	发电成本/美元	污染排放/磅
10机组	MOPSO	2 145 703	213 290
	NSGA-II	2 102 216	212 587
	MODEA	2 097 965	201 870
	IBBMOPSO	2 131 663	208 182
	CMODE	2 105 256	207 677
	CMMODE	2 068 974	210 868
15机组	MOPSO	577 429	341 388
	NSGA-II	585 056	160 039
	MODEA	578 193	152 180
	IBBMOPSO	576 299	139 222
	CMODE	578 460	194 622
	CMMODE	576 727	131 374

系统	算法	发电成本/美元	污染排放/磅
10机组	MOPSO	3 319 575	197 635
	NSGA-II	2 888 229	195 127
	MODEA	2 172 361	194 748
	IBBMOPSO	3 089 211	191 034
	CMODE	2 375 165	193 625
	CMMODE	2 185 388	193 619
15机组	MOPSO	595 900	150 450
	NSGA-II	598 623	142 879
	MODEA	589 984	137 097
	IBBMOPSO	582 326	136 077
	CMODE	596 683	144 489
	CMMODE	590 707	127 282

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